Modeling and Topology Estimation of Low Rank Dynamical Networks

Wenqi Cao; Aming Li

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arXiv · 2511.06674

Modeling and Topology Estimation of Low Rank Dynamical Networks

Wenqi Cao·Aming Li

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Abstract

Conventional topology learning methods for dynamical networks become inapplicable to processes exhibiting low-rank characteristics. To address this, we propose the low rank dynamical network model which ensures identifiability. By employing causal Wiener filtering, we establish a necessary and sufficient condition that links the sparsity pattern of the filter to conditional Granger causality. Building on this theoretical result, we develop a consistent method for estimating all network edges. Simulation results demonstrate the parsimony of the proposed framework and consistency of the topology estimation approach.

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Wenqi Cao, Aming Li. 2025-11-10. Modeling and Topology Estimation of Low Rank Dynamical Networks. https://arxiv.org/abs/2511.06674

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